\begin{abstract}
Collaborative Filtering (CF) has achieved widespread success in
recommender systems, such as Amazon and Yahoo! music. However, CF
usually suffers from two fundamental problems - data sparsity and
limited scalability. Among the two broad classes of CF approaches,
namely, memory-based and model-based, the former usually falls short of
the system scalability demands, because these approaches predict user
preferences over the entire item-user matrix. The latter often achieves
unsatisfactory accuracy, because they cannot precisely capture the
diversity in user rating styles.

In this paper, we propose an efficient Collaborative Filtering approach
using Smoothing and Fusing (CFSF) strategies. CFSF formulates CF
problem as a local prediction problem by mapping it from the entire
large-scale item-user matrix to a locally reduced item-user matrix.
Given an active item and a user, CFSF constructs a local item-user
matrix as the basis of prediction. To alleviate the data sparsity, CFSF
presents fusion strategy for the local item-user matrix, which fuses
the ratings of the same user makes on the similar items, and the
like-minded users make on the same as well as similar items. To
eliminate the diversity in user rating styles, CFSF uses smoothing
strategy that clusters users over the entire item-user matrix and then
smoothes ratings within each user cluster. Empirical study shows that
CFSF outperforms the state-of-the-art CF approaches in terms of
accuracy and scalability.

%CF approaches in terms of accuracy and scalability.

%Thus, the scale of problem is significantly reduced.


\end{abstract}

% A category with the (minimum) three required fields
\category{H.3.3}{Information Storage and Retrieval}{Information Search
and Retrieval}[Information Filtering] \terms{Approaches, Performance,
Experimentation} \keywords{Collaborative Filtering, Clustering, Fusion,
Smoothing}
